Automated identification of Ichneumonoidea wasps via YOLO-based deep learning: Integrating HiresCam for Explainable AI

• URL: http://arxiv.org/abs/2603.16351v1
• Date: Tue, 17 Mar 2026 10:34:46 GMT
• Title: Automated identification of Ichneumonoidea wasps via YOLO-based deep learning: Integrating HiresCam for Explainable AI
• Abstract summary: This study proposes a deep learning-based framework for the automated identification of Ichneumonoidea wasps.<n>The proposed system simultaneously identifies wasp families from high-resolution images.<n>The results demonstrate high accuracy of over 96 % and robust generalization across morphological variations.
• Score: 0.5673416639353487
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate taxonomic identification of parasitoid wasps within the superfamily Ichneumonoidea is essential for biodiversity assessment, ecological monitoring, and biological control programs. However, morphological similarity, small body size, and fine-grained interspecific variation make manual identification labor-intensive and expertise-dependent. This study proposes a deep learning-based framework for the automated identification of Ichneumonoidea wasps using a YOLO-based architecture integrated with High-Resolution Class Activation Mapping (HiResCAM) to enhance interpretability. The proposed system simultaneously identifies wasp families from high-resolution images. The dataset comprises 3556 high-resolution images of Hymenoptera specimens. The taxonomic distribution is primarily concentrated among the families Ichneumonidae (n = 786), Braconidae (n = 648), Apidae (n = 466), and Vespidae (n = 460). Extensive experiments were conducted using a curated dataset, with model performance evaluated through precision, recall, F1 score, and accuracy. The results demonstrate high accuracy of over 96 % and robust generalization across morphological variations. HiResCAM visualizations confirm that the model focuses on taxonomically relevant anatomical regions, such as wing venation, antennae segmentation, and metasomal structures, thereby validating the biological plausibility of the learned features. The integration of explainable AI techniques improves transparency and trustworthiness, making the system suitable for entomological research to accelerate biodiversity characterization in an under-described parasitoid superfamily.

Related papers

• ViroBench: Benchmarking Nucleotide Foundation Models on Viral Genomics Tasks [86.89727311669937]
  We introduce ViroBench, the first comprehensive and large-scale benchmark specifically designed for nucleotide foundation models (NFMs)<n>ViroBench evaluates models across two critical dimensions: biological understanding and latent biosecurity risk, covering 18 diverse scenarios within 4 task types.<n>ViroBench provides interpretable, diagnostic evaluations and a reproducible measurement framework for future research on viral nucleotide foundation models.
  arXiv  Detail & Related papers  (2026-05-25T03:31:46Z)
• Automatic Image-Level Morphological Trait Annotation for Organismal Images [11.844505008466884]
  We introduce a trait annotation pipeline that localizes salient regions and uses vision-language prompting to generate interpretable trait descriptions.<n>We construct Bioscan-Traits, a dataset of 80K trait annotations spanning 19K insect images from BIOSCAN-5M.
  arXiv  Detail & Related papers  (2026-04-02T04:55:52Z)
• A multi-modal dataset for insect biodiversity with imagery and DNA at the trap and individual level [12.817729932901779]
  We present the Mixed Arthropod Sample and Identification (MassID45) dataset for training automatic classifiers of bulk insect samples.<n>It uniquely combines molecular and imaging data at both the unsorted sample level and the full set of individual specimens.<n>Human annotators, supported by an AI-assisted tool, performed two tasks on bulk images: creating segmentation masks around each individual arthropod and assigning taxonomic labels to over 17 000 specimens.
  arXiv  Detail & Related papers  (2025-07-09T16:03:06Z)
• BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning [60.80381372245902]
  We find emergent behaviors in biological vision models via large-scale contrastive vision-language training.<n>We train BioCLIP 2 on TreeOfLife-200M to distinguish different species.<n>We identify emergent properties in the learned embedding space of BioCLIP 2.
  arXiv  Detail & Related papers  (2025-05-29T17:48:20Z)
• BeetleVerse: A Study on Taxonomic Classification of Ground Beetles [0.310688583550805]
  Ground beetles are a highly sensitive and speciose biological indicator, making them vital for monitoring biodiversity.<n>In this paper, we evaluate 12 vision models on taxonomic classification across four diverse, long-tailed datasets.<n>Our results show that the Vision and Language Transformer combined with an head is the best performing model, with 97% accuracy at genus and species level.
  arXiv  Detail & Related papers  (2025-04-18T01:06:37Z)
• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)
• Neural Echos: Depthwise Convolutional Filters Replicate Biological Receptive Fields [56.69755544814834]
  We present evidence suggesting that depthwise convolutional kernels are effectively replicating the biological receptive fields observed in the mammalian retina. We propose a scheme that draws inspiration from the biological receptive fields.
  arXiv  Detail & Related papers  (2024-01-18T18:06:22Z)
• Comparison between transformers and convolutional models for fine-grained classification of insects [7.107353918348911]
  We consider the taxonomical class of Insecta. The identification of insects is essential in biodiversity monitoring as they are one of the inhabitants at the base of many ecosystems. We have billions of images that need to be automatically classified and deep neural network algorithms are one of the main techniques explored for fine-grained tasks.
  arXiv  Detail & Related papers  (2023-07-20T10:00:04Z)
• Taxonomy and evolution predicting using deep learning in images [9.98733710208427]
  This study creates a novel recognition framework by systematically studying the mushroom image recognition problem. We present the first method to map images to DNA, namely used an encoder mapping image to genetic distances, and then decoded DNA through a pre-trained decoder.
  arXiv  Detail & Related papers  (2022-06-28T13:54:14Z)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.